U.S. patent application number 12/052977 was filed with the patent office on 2008-07-10 for method and system for determining offering combinations in a multi-product environment.
Invention is credited to Jianying HU, Aleksandra Mojsilovic.
Application Number | 20080167951 12/052977 |
Document ID | / |
Family ID | 37695463 |
Filed Date | 2008-07-10 |
United States Patent
Application |
20080167951 |
Kind Code |
A1 |
HU; Jianying ; et
al. |
July 10, 2008 |
METHOD AND SYSTEM FOR DETERMINING OFFERING COMBINATIONS IN A
MULTI-PRODUCT ENVIRONMENT
Abstract
A multi-product environment is analyzed to identify combinations
of products or services which represent strategic offerings of a
company. For a multi-product environment and a set of client
accounts, a segmentation tree is constructed to identify the
offering groups of interest. The tree is first initialized as a
root representing all offerings, all clients and an empty offering
set. A recursive algorithm is then applied to grow the tree at each
node by segmenting the clients based on whether a particular
offering is purchased. The selection of the offering to use for
segmentation at each node is determined by a mathematical algorithm
that considers two factors: 1) the offering should have high
pulling power, meaning it is likely to produce high revenue in
combination with other offerings, and 2) the offering should be
unlikely to cause fragmentation, meaning nodes representing a very
small amount of revenue. The algorithm terminates when each leaf
node reaches one of the two limits: 1) Representation limit which
is reached when a significant portion of revenue is accounted for
by offerings in a particular grouping and 2) Significance limit
which is reached when the revenue represented by a node is too
small to be considered significant. At this point all leaf nodes
representing significant revenue are collected as the offering
groups.
Inventors: |
HU; Jianying; (Bronx,
NY) ; Mojsilovic; Aleksandra; (New York, NY) |
Correspondence
Address: |
Whitham, Curtis, & Christofferson, P.C.
11491 Sunset Hills Road, Suite 340
Reston
VA
20190
US
|
Family ID: |
37695463 |
Appl. No.: |
12/052977 |
Filed: |
March 21, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11191081 |
Jul 28, 2005 |
|
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12052977 |
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Current U.S.
Class: |
705/26.62 |
Current CPC
Class: |
G06Q 30/02 20130101;
G06Q 30/0202 20130101; G06Q 30/0625 20130101 |
Class at
Publication: |
705/10 ;
705/7 |
International
Class: |
G06Q 10/00 20060101
G06Q010/00; G06F 17/00 20060101 G06F017/00 |
Claims
1. A method for determining offering combinations in a
multi-product environment comprising the steps of: analyzing
multiple products or services provided by a company; identifying
offering combinations of products or services that maximize a
coverage of a quantifiable business objective; and collecting
offering combinations of a quantifiable business objective.
2. The method recited in claim 1, wherein the step of identifying
comprises the step of constructing a segmentation tree using a
recursive algorithm to identify the offering combinations and the
step of collecting the offering combinations represented by all
leaf nodes of the segmentation.
3. The method recited in claim 1, wherein the step of collecting is
performed using a quantifiable business objective exceeding a
predetermined threshold.
4. The method recited in claim 3, wherein the quantifiable business
objective is selected from the group consisting of amount of
company's revenue, profit, and inventory.
5. The method recited in claim 1, further comprising the steps of:
initializing the segmentation tree as a root representing all
offerings, all clients and an empty offering set; and applying the
recursive algorithm to grow the tree at each node by segmenting the
clients based on whether a particular offering is purchased.
6. The method recited in claim 5, further comprising the step of
selecting an offering to use for segmentation at each node based on
an algorithm that considers the pulling power of the offering,
where a high pulling power means that the offering is likely to
produce high revenue in combination with other offerings, and
fragmentation of the offering, where an low fragmentation means
that the offering is unlikely to lead to fragmented nodes.
7. The method recited in claim 1, wherein the step of constructing
a segmentation tree is stopped at a node if at least one of the
following limits is reached: 1) Coverage limit, which is reached
when percentage of revenue of grouped products over total revenue
for clients represented by the node is larger than a preselected
threshold, or 2) Significance limit, which is reached when
percentage of revenue represented by the node over total revenue is
less than a preselected threshold.
8. A computer system for determining offering combinations in a
multi-product environment comprising: a database storing
information on products and services provided by a company; a
programmed processor which accesses the database and analyzes the
information on products and services, said programmed processor
identifying offering combinations of products or services that
maximize a coverage of a quantifiable business objective; and a
display which displays offering combinations with a quantifiable
business objective collected by the programmed processor.
9. The computer system recited in claim 8, wherein said programmed
processor constructs a segmentation tree using a recursive
algorithm to identify the offering combinations and collects the
offering combinations represented by all leaf nodes of the
segmentation.
10. The computer system recited in claim 8, wherein said programmed
processor uses a quantifiable business objective exceeding a
predetermined threshold when collecting the offering
combinations.
11. The computer system recited in claim 8, wherein the programmed
processor first initializes the segmentation tree as a root
representing all offerings, all clients and an empty offering set,
and then applies the recursive algorithm to grow the tree at each
node by segmenting the clients based on whether a particular
offering is purchased.
12. The computer system recited in claim 11, wherein the programmed
processor selects an offering to use for segmentation at each node
based on an algorithm that considers the pulling power of the
offering, where a high pulling power means that the offering is
likely to produce high revenue in combination with other offerings,
and fragmentation of the offering, where an low fragmentation means
that the offering is unlikely to lead to fragmented nodes.
13. The computer system recited in claim 1, wherein the programmed
processor stops constructing a segmentation tree at a node if at
least one of the following limits is reached: 1) coverage limit,
which is reached when percentage of revenue of grouped products
over total revenue for clients represented by the node is larger
than a preselected threshold, or 2) significance limit, which is
reached when percentage of revenue represented by the node over
total revenue is less than a preselected threshold.
14. A computer readable medium having computer code for performing
a process on a computer for determining offering combinations in a
multi-product environment, the process comprising the steps of:
analyzing multiple products or services provided by a company;
identifying offering combinations of products or services that
maximize a coverage of a quantifiable business objective; and
collecting offering combinations of a quantifiable business
objective.
15. The computer readable medium recited in claim 14, wherein the
step of identifying comprises the step of constructing a
segmentation tree using a recursive algorithm to identify the
offering combinations and the step of collecting collects the
offering combinations represented by all leaf nodes of the
segmentation.
16. The computer readable medium recited in claim 14, wherein the
step of collecting is performed using a quantifiable business
objective exceeding a predetermined threshold.
17. The computer readable medium recited in claim 14, wherein in
the process performed by the computer code further comprises the
steps of: initializing the segmentation tree as a root representing
all offerings, all clients and an empty offering set; and applying
the recursive algorithm to grow the tree at each node by segmenting
the clients based on whether a particular offering is
purchased.
18. The computer readable medium recited in claim 17, wherein the
process performed by the code further comprises the step of
selecting an offering to use for segmentation at each node based on
an algorithm that considers the pulling power of the offering,
where a high pulling power means that the offering is likely to
produce high revenue in combination with other offerings, and
fragmentation of the offering, where an low fragmentation means
that the offering is unlikely to lead to fragmented nodes.
19. The computer readable medium recited in claim 14, wherein the
step of constructing a segmentation tree is stopped at a node if at
least one of the following limits is reached: 1) coverage limit,
which is reached when percentage of revenue of grouped products
over total revenue for clients represented by the node is larger
than a preselected threshold, or 2) significance limit, which is
reached when percentage of revenue represented by the node over
total revenue is less than a preselected threshold.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention generally relates to a process for
assisting companies with a diverse set of products and solutions in
identifying strategic offerings (e.g., combinations of products or
solutions that consistently drive a significant portion of
company's revenue and represent significant client base) and, more
particularly, to a methodology for determining strategically
important purchasing patterns within the company's client base to
further guide strategic decisions about effectively positioning
company's products and services as stand-alone offerings, and
increasing the efficiency of the organization by further exploiting
and promoting cross-selling and cross-marketing of company's
products and services.
[0003] 2. Background Description
[0004] Today, marketing strategies of most companies and
enterprises depend on customer segmentation, i.e., in understanding
characteristics and behavioral patterns of their customers. Various
methodologies to gather such knowledge have been developed over the
years. Customer segmentation is a process of identifying
homogeneous groups within company's customer base in order to
develop unique proposition matching the needs of each segment. The
fundamental goal of traditional market segmentation methodologies
is to identify groups, segments or clusters of customers, which,
from a marketing perspective, are meaningfully different from each
other in terms of purchasing habits, product preferences,
likelihood to buy, motivation, loyalty to the company's products
and services, or present and future value to the company.
[0005] One of the standard approaches in market segmentation is the
use of data mining, statistical analysis and pattern recognition
methodologies to discover different clusters and identify their
discriminating characteristics. Segmentation criteria typically
include demographic information, lifestyle and life-stage data,
buying factors, needs, lifestyles, behavioral information, etc.
Following the customer segmentation, propensity models (models
comparing the attributes of prospects lists to the attributes of
existing customers) are often developed by businesses and used to
develop target lists of persons who look like existing customers,
and therefore might have a greater propensity to respond to
marketing initiatives and buy company's product or service.
Therefore, customer segmentation is typically perceived as a
marketing tool for customer portfolio management, product
development, marketing strategy, and promotional and targeting
decisions, and has not been considered as affecting the whole
corporation strategically. It is typically conducted to answer the
following questions, who are my most loyal clients, which segments
should we target, how can we manage customer segments by allocating
resources among them, who are my new most likely customers. Thus,
market segmentation has been used more as a tactical device than a
strategic decision support tool. Recently, a new opportunity for
pattern recognition as a support tool in strategic decision making
process has arisen. As more and more companies diversify their
operations and expand the spectrum of their products and services,
it is becoming critical to understand cross-cohesion among
different products/services and identify natural groupings of
products/service that were not expected to exist or have not been
addressed in the development phase of each individual product.
Rather than helping develop marketing strategies for a particular
product, or a certain customer segment, such knowledge is far more
important as it could guide strategic decisions at the top levels
of corporation, optimize the behavior of the entire enterprise by
exploiting the linkages between different brands, institute new
offerings by "bundling" the discovered combinations of
products/services, and even open up new markets and new
opportunities driven by the identified relationships.
[0006] We will clarify this problem through an example of a large
hardware company. Over the course of several decades, the company
has developed and launched a number of different hardware products
and related equipment: mainframes, super-computers, personal
computers, small computing devices, storage devices, etc. As the
market grew, the company grew as well and began to add a variety of
software offerings and operating systems to maintain and support
systems they are selling. Soon, the operating systems evolved to
include more sophisticated productivity applications, relational
databases, programming environments and software suits supporting
various tasks on company's computers. As these products became more
and more popular, the software packages evolved further and became
independent on company's operating platforms, thus running on a
variety of different (often competitive) systems. Naturally, the
company decided to expand and in addition to the existing hardware
divisions, it instituted several different software groups. As the
information technology further advanced, and as systems became more
and more complicated, calling for the integration of multiple
platforms and applications, the company realized the value of
technical support, help desk operations, maintenance, and
technology consulting, and started to offer these services through
a variety of newly instituted divisions. Thus, the company that was
once viewed as "pure" hardware and equipment manufacturer became a
conglomerate of different units, each representing and running as
an individual company. Each unit had its own strategy, goals and
measurements, management, marketing and sales force. However,
although these individual units were designed to run independently
and serve their customer base, the company management soon realized
that some of the seemingly unrelated products are often purchased
together. So the question quickly became, in such a diverse
multi-product environment, is it possible to segment the space of
products and services and determine the combinations, which are
tend to be bought together, and which represent significant
components of the total company earnings? Companies could benefit
enormously from identifying cross-cohesion in such a diverse
multi-product environments. They could eliminate organizational
inconsistencies, optimize their marketing and sales efforts,
institute new offerings and influence the strategic directions of
the corporation. Our hardware company is just one example of
something that is becoming a trend in today's market place. This
kind of diverse behavior is representative for large corporations
across all economic sectors, for example, it is often seen in banks
and financial services organizations, insurance, even
manufacturing.
[0007] Note that the described segmentation problem is very
different in nature from the traditional market segmentation or
shopping-basket analysis techniques, which attempt to identify
items that are frequently bought together. These traditional
approaches apply data-mining, statistical analysis and pattern
recognition to detect most frequent combinations of purchases by
analyzing millions and millions of transactions in a data
warehouse. In our case of product/services analyses, we are looking
into a customer base of a single company (thus a far smaller data
set will be analyzed) and many of the traditional approaches cannot
be applied. Furthermore, rather then mining for the most frequent
combinations of purchases, companies are interested in the
combinations that have the most significant impact on the
bottom-line revenue. Finally, companies are also looking to
identify products and services that are likely to drive the
purchase of additional products and services sometime in the
future, a problem that also cannot be addressed by standard
shopping basket analysis. This problem is also very different in
nature from the current segmentation methodologies, as they focus
on a client portfolio and its value to the organization. Therefore,
when applied to segment company's products and services these
methodologies produce sub-optimal results. Hence, it is very
important to develop an approach for segmenting company's
individual products/services in order to identify the important
cross-cohesion drivers in overall performance.
SUMMARY OF THE INVENTION
[0008] It is therefore an object of the present invention to
provide a process or methodology for analyzing a multi-product
environment and identify the combinations of products/services,
which represent strategic offerings of a company. One example of
strategic offerings are combinations of products/services, which
represent significant amount of company's total revenue and span a
considerable portion of its client base.
[0009] According to the invention, for a multi-product environment
and a set of client accounts, a segmentation tree is constructed to
identify the offering groups of interest. The tree is first
initialized as a root representing all offerings, all clients and
an empty offering set. A recursive algorithm is then applied to
grow the tree at each node by segmenting the clients based on
whether a particular offering is purchased. The selection of the
offering to use for segmentation at each node is determined by a
mathematical algorithm that considers two factors: 1) the offering
should have high pulling power, meaning it is likely to produce
high revenue in combination with other offerings, and 2) the
offering should be unlikely to cause fragmentation, meaning nodes
representing a very small amount of revenue. The algorithm
terminates when each leaf node reaches one of the two limits: 1)
Representation limit which is reached when a significant portion of
revenue is accounted for by offerings in a particular grouping and
2) Significance limit which is reached when the revenue represented
by a node is too small to be considered significant. At this point
all leaf nodes representing significant revenue are collected as
the offering groups.
[0010] Compared to previous methods such as market basket analysis,
this algorithm has the advantage that it is able to identify groups
of offerings where the offerings in each group not only occur
together often, but more importantly contribute a significant
amount of revenue. Furthermore, all the offering groups taken
together span a significant portion of the client base.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The foregoing and other objects, aspects and advantages will
be better understood from the following detailed description of a
preferred embodiment of the invention with reference to the
drawings, in which:
[0012] FIG. 1 is a block diagram of a computer system on which the
method according to the invention may be implemented;
[0013] FIG. 2 is a block diagram of a server used in the computer
system shown in FIG. 1;
[0014] FIG. 3 is a block diagram of a client used in the computer
system shown in FIG. 1;
[0015] FIG. 4 is a flow diagram showing the overall logic of the
method according to the invention;
[0016] FIG. 5 is a flow diagram showing the logic of the children
generating procedure used in the method illustrated in FIG. 4;
[0017] FIG. 6 is a flow diagram showing the logic of the
computation of the "pulling factor" of an offering at a particular
node in the method illustrated in FIG. 4; and
[0018] FIG. 7 is a flow diagram showing the logic of the
computation of the "fragmentation factor" of an offering at a
particular node in the method illustrated in FIG. 4.
DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION
[0019] Referring now to the drawings, and more particularly to FIG.
1, there is shown a computer system on which the method according
to the invention may be implemented. Computer system 100 contains a
network 102, which is the medium used to provide communications
links between various devices and computers connected together
within computer system 100. Network 102 may include permanent
connections, such as wire or fiber optic cables, wireless
connections, such as wireless Local Area Network (WLAN) products
based on the IEEE 802.11 specification (also known as Wi-Fi),
and/or temporary connections made through telephone, cable or
satellite connections, and may include a Wide Area Network (WAN)
and/or a global network, such as the Internet. A server 104 is
connected to network 102 along with storage unit 106. In addition,
clients 108, 110 and 112 also are connected to network 102. These
clients 108, 110 and 112 may be, for example, personal computers or
network computers. For purposes of this application, a network
computer is any computer, coupled to a network, which receives a
program or other application from another computer coupled to the
network. The server 104 provides data, such as boot files,
operating system images, and applications to clients 108, 110 and
112. Clients 108, 110 and 112 are clients to server 104.
[0020] Computer system 100 may include additional servers, clients,
and other devices not shown. In the depicted example, the Internet
provides the network 102 connection to a worldwide collection of
networks and gateways that use the TCP/IP (Transmission Control
Protocol/Internet Protocol) suite of protocols to communicate with
one another. At the heart of the Internet is a backbone of
high-speed data communication lines between major nodes or host
computers, consisting of thousands of commercial, government,
educational and other computer systems that route data and
messages. In this type of network, hypertext mark-up language
(HTML) documents and applets are used to exchange information and
facilitate commercial transactions. Hypertext transfer protocol
(HTTP) is the protocol used in these examples to send data between
different data processing systems. Of course, computer system 100
also may be implemented as a number of different types of networks
such as, for example, an intranet, a local area network (LAN), or a
wide area network (WAN). FIG. 1 is intended as an example, and not
as an architectural limitation for the present invention.
[0021] Referring to FIG. 2, a block diagram of a data processing
system that may be implemented as a server, such as server 104 in
FIG. 1, is depicted in accordance with a preferred embodiment of
the present invention. Server 200 may be used to execute any of a
variety of business processes. Server 200 may be a symmetric
multiprocessor (SMP) system including a plurality of processors 202
and 204 connected to system bus 206. Alternatively, a single
processor system may be employed. Also connected to system bus 206
is memory controller/cache 208, which provides an interface to
local memory 209. Input/Output (I/O) bus bridge 210 is connected to
system bus 206 and provides an interface to I/O bus 212. Memory
controller/cache 208 and I/O bus bridge 210 may be integrated as
depicted.
[0022] Peripheral component interconnect (PCI) bus bridge 214
connected to I/O bus 212 provides an interface to PCI local bus
216. A number of modems may be connected to PCI bus 216. Typical
PCI bus implementations will support four PCI expansion slots or
add-in connectors. Communications links to network computers 108,
110 and 112 in FIG. 1 may be provided through modem 218 and network
adapter 220 connected to PCI local bus 216 through add-in
boards.
[0023] Additional PCI bus bridges 222 and 224 provide interfaces
for additional PCI buses 226 and 228, from which additional modems
or network adapters may be supported. In this manner, server 200
allows connections to multiple network computers. A graphics
adapter 230 and hard disk 232 may also be connected to I/O bus 212
as depicted, either directly or indirectly.
[0024] Those of ordinary skill in the art will appreciate that the
hardware depicted in FIG. 2 may vary. For example, other peripheral
devices, such as optical disk drives and the like, also may be used
in addition to or in place of the hardware depicted. The depicted
example is not meant to imply architectural limitations with
respect to the present invention.
[0025] The data processing system depicted in FIG. 2 may be, for
example, an IBM RISC/System 6000 system, a product of International
Business Machines Corporation in Armonk, N.Y., running the Advanced
Interactive Executive (AIX) operating system.
[0026] With reference now to FIG. 3, a block diagram illustrating a
client computer is depicted in accordance with a preferred
embodiment of the present invention. Client computer 300 employs a
peripheral component interconnect (PCI) local bus architecture.
Although the depicted example employs a PCI bus, other bus
architectures such as Accelerated Graphics Port (AGP) and Industry
Standard Architecture (ISA) may be used. Processor 302 and main
memory 304 are connected to PCI local bus 306 through PCI bridge
308. PCI bridge 308 also may include an integrated memory
controller and cache memory for processor 302. Additional
connections to PCI local bus 306 may be made through direct
component interconnection or through add-in boards.
[0027] In the depicted example, local area network (LAN) adapter
310, Small Computer System Interface (SCSI) host bus adapter 312,
and expansion bus interface 314 are connected to PCI local bus 306
by direct component connection. In contrast, audio adapter 316,
graphics adapter 318, and audio/video adapter 319 are connected to
PCI local bus 306 by add-in boards inserted into expansion slots.
Expansion bus interface 314 provides a connection for a keyboard
and mouse adapter 320, modem 322, and additional memory 324. SCSI
host bus adapter 312 provides a connection for hard disk drive 326,
tape drive 328, and CD-ROM drive 330. Typical PCI local bus
implementations will support three or four PCI expansion slots or
add-in connectors.
[0028] An operating system runs on processor 302 and is used to
coordinate and provide control of various components within data
processing system 300 in FIG. 3. The operating system may be a
commercially available operating system, such as Windows XP, which
is available from Microsoft Corporation. An object-oriented
programming system such as Java may run in conjunction with the
operating system and provides calls to the operating system from
Java programs or applications executing on data processing system
300. "Java" is a trademark of Sun Microsystems, Inc. Instructions
for the operating system, the object-oriented operating system, and
applications or programs are located on storage devices, such as
hard disk drive 326, and may be loaded into main memory 304 for
execution by processor 302.
[0029] Those of ordinary skill in the art will appreciate that the
hardware in FIG. 3 may vary depending on the implementation. Other
internal hardware or peripheral devices, such as flash ROM (or
equivalent nonvolatile memory) or optical disk drives and the like,
and/or I/O devices, such as Universal Serial Bus (USB) and IEEE
1394 devices, may be used in addition to or in place of the
hardware depicted in FIG. 3. Also, the processes of the present
invention may be applied to a multiprocessor data processing
system.
[0030] Data processing system 300 may take various forms, such as a
stand alone computer or a networked computer. The depicted example
in FIG. 3 and above-described examples are not meant to imply
architectural limitations.
[0031] FIG. 4 shows the overall logic of the method according to
the invention. The process begins function block 400 where
historical data containing the revenue for each client derived from
each product is compiled. Insignificant purchases (purchases with
very small amount of revenue) are filtered out. In function block
402, a binary tree is initialized, and all clients and an empty
offering set are assigned to the root. In function block 404, a
mask is created consisting of M binary fields where M is the total
number of offerings. All binary fields of the mask are set to 0
(meaning no offering has been used to segment clients).
[0032] At this point in the process, a children generating
procedure is recursively carried out at each node at function block
406. This process is shown in more detail in FIG. 5, to which
reference is now made. The children generating procedure begins at
input block 500 where a tree node is input. C represents the set of
clients at this node, O represents the group of offerings purchased
by the client, and M is a vector of binary values (called masking
values), where a masking value of 1 indicates the corresponding
offering has already been used in client segmentation, and 0
indicates otherwise. The output 510 is two children of the node,
where Cl, Ol, and Ml represent the client set, offering group and
mask represented by the left child, and Cr, Or, Mr represent the
client set, offering group and mask represented by the right child,
respectively, and the union of Cl and Cr equals C.
[0033] There are four steps in the process. First, at function
block 502, the set of valid offerings, which are offerings whose
masking values equal 0, is collected. Then for each offering, the
pulling factor (P) and the fragmentation factor (F) are computed in
function block 504, as explained with reference to FIGS. 6 and 7.
For each offering, it's overall segmentation is assigned a score
(S) to P*F in function block 506. Then, in function block 508, the
offering with the highest segmentation score (S), called Os, is
selected, and two children are generated such that the clients who
have purchased Os are assigned to Cl and those who have not
purchased offering Os are assigned to Cr. The corresponding masking
value is set to 1 in Ml and Mr, and Ol and Or are updated
accordingly.
[0034] FIG. 6 illustrates the process of computation of the
"pulling factor" of an offering at a particular node. A higher
value for "pulling factor" indicates higher correlation between
this offering and its top N most correlated offerings. N is a
preselected number, typically around 10% of the total number of
offerings. The process begins at input block 600 where an offering
and a node are input. The output 608 is the pulling factor for the
given offering at the given node. The process comprises three
steps. The first step at function block 602 is, for each valid
offering, computing its correlation ratio with the given offering.
Next, at function block 604, the top N offerings with the highest
correlation ratio are identified. Then, at function block 606, the
correlated revenue from these N offerings are aggregated and
returned as the pulling factor for the given offering.
[0035] FIG. 7 illustrates the process of computation of the
"fragmentation factor" of an offering at a particular node. A
higher value of "fragmentation factor" indicates this offering is
more likely to lead to fragmented nodes. The process begins at
input block 700 where an offering and a node are input. The output
704 is the fragmentation factor for given offering at given node.
Here, the definition of the fragmentation factor is given as
alpha+pow(R, Beta), where:
[0036] alpha: shift parameter, typical value: 2
[0037] beta: slope parameter, typical value: 0.1
[0038] R: a ratio measuring how close the node is to reaching the
"significance limit", with 0 indicating the limit is reached, and 1
indicating it is far from reaching the limit.
[0039] One possible implementation of the computation in function
block 702 is given as follows:
[0040] R=min{(Rev-T)/T, 1.0}, where Rev is the revenue from clients
who do not purchase the given offering, and T is a threshold
indicating a very small revenue.
[0041] Returning now to FIG. 4, the children generating procedure
at a node is stopped if at least one of the following limits is
reached:
[0042] 1) Coverage limit is reached when percentage of revenue of
grouped products over total revenue for clients represented by this
node is larger than a preselected threshold (e.g., 80%), as
determined in decision block 408, or
[0043] 2) Significance limit is reached when percentage of revenue
represented by the node over total revenue is less than a
preselected threshold (e.g., 0.5%), as determined in decision block
410.
[0044] The last step in the process at function block 412 is to
collect the offering combinations represented by all leaf nodes
with significant revenue, i.e., nodes that do not reach the
significance limit. The collected offering combinations are
displayed, printed or otherwise output to a user. Typically, the
display would be on the display of a client computer, but those
skilled in the art will recognize that other outputs, including
printing, are the full equivalent of a display, and the tangible
output provided is useful to assist in making decisions in product
offerings.
[0045] While the invention has been described in terms of a single
preferred embodiment, those skilled in the art will recognize that
the invention can be practiced with modification within the spirit
and scope of the appended claims.
* * * * *